Date of Publication :10th November 2017

Abstract: This Survey investigates how quantitative user data, extracted from server logs, and clustering algorithms can be used to model and understand user-behavior. The Survey also investigates how the results compare to the more traditional method of qualitative user-behavior analysis through observations. The results show that clustering of all user data, only a small subset of users increases the reliability of findings. However, the quantitative method has a risk of missing important insights that can only be discovered through observation of the user. The conclusion drawn in this survey is that a combination of both is necessary to truly understand the user-behavior.

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